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In this study, the dual- and self-curing potential of self-adhesive resin cements (SARCs) as thin, clinically-relevant cement films was investigated. The SARCs tested were: BisCem (BSC; Bisco), Maxcem Elite (MXE; Kerr), RelyX Unicem clicker (UNI; 3M ESPE), seT capsule (SET; SDI), and SmartCem 2 (SC2; Dentsply Caulk). The conventional cement RelyX ARC (3M ESPE) was tested as a reference. The degree of conversion (DC) as a function of time was evaluated by real-time Fourier transform infrared spectroscopy with an attenuated total reflectance (ATR) device. The cements were either photoactivated for 40 seconds (dual-cure mode) or not photoactivated (self-cure mode). The cement film thickness was 50 ± 10 μm. The DC (%) was evaluated 1, 5, 10, 15, 20, 25, and 30 minutes after placing the cement on the ATR cell. Data for DC as a function of time were analyzed by two-way repeated measures analysis of variance (ANOVA). DC values at 30 minutes for the self- and dual-cure modes were submitted to one-way ANOVA. Post hoc comparisons were performed using the Student-Newman-Keuls test (p<0.05). The rate and the extent of conversion were lower for the SARCs compared with the conventional cement. Means ± standard deviations (SD) for the dual-cure mode at 30 minutes were: 75 ± 5 (ARC)a, 73 ± 8 (SET)a, 61 ± 4 (MXE)b, 51 ± 9 (BSC)c, 51 ± 4 (UNI)c, and 48 ± 3 (SC2)c, while in the self-cure mode means and SD were 62 ± 6 (ARC)a, 54 ± 3 (MXE)b, 40 ± 6 (SC2)c, 35 ± 2 (UNI)c, 35 ± 3 (SET)c, and 11 ± 3 (BSC)d. The DC for the dual-cure mode was generally higher than the self-cure, irrespective of the time. Discrepancies in DC between the dual- and self-cure modes from 11% to 79% were observed. In conclusion, SARCs may present slower rate of polymerization and lower final DC than conventional resin cements, in either the dual- or self-cure mode.

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Background Despite the prevalence of mobile health (mHealth) technologies and observations of their impacts on patients’ health, there is still no consensus on how best to evaluate these tools for patient self-management of chronic conditions. Researchers currently do not have guidelines on which qualitative or quantitative factors to measure or how to gather these reliable data. Objective This study aimed to document the methods and both qualitative and quantitative measures used to assess mHealth apps and systems intended for use by patients for the self-management of chronic noncommunicable diseases. Methods A scoping review was performed, and PubMed, MEDLINE, Google Scholar, and ProQuest Research Library were searched for literature published in English between January 1, 2015, and January 18, 2019. Search terms included combinations of the description of the intention of the intervention (eg, self-efficacy and self-management) and description of the intervention platform (eg, mobile app and sensor). Article selection was based on whether the intervention described a patient with a chronic noncommunicable disease as the primary user of a tool or system that would always be available for self-management. The extracted data included study design, health conditions, participants, intervention type (app or system), methods used, and measured qualitative and quantitative data. Results A total of 31 studies met the eligibility criteria. Studies were classified as either those that evaluated mHealth apps (ie, single devices; n=15) or mHealth systems (ie, more than one tool; n=17), and one study evaluated both apps and systems. App interventions mainly targeted mental health conditions (including Post-Traumatic Stress Disorder), followed by diabetes and cardiovascular and heart diseases; among the 17 studies that described mHealth systems, most involved patients diagnosed with cardiovascular and heart disease, followed by diabetes, respiratory disease, mental health conditions, cancer, and multiple illnesses. The most common evaluation method was collection of usage logs (n=21), followed by standardized questionnaires (n=18) and ad-hoc questionnaires (n=13). The most common measure was app interaction (n=19), followed by usability/feasibility (n=17) and patient-reported health data via the app (n=15). Conclusions This review demonstrates that health intervention studies are taking advantage of the additional resources that mHealth technologies provide. As mHealth technologies become more prevalent, the call for evidence includes the impacts on patients’ self-efficacy and engagement, in addition to traditional measures. However, considering the unstructured data forms, diverse use, and various platforms of mHealth, it can be challenging to select the right methods and measures to evaluate mHealth technologies. The inclusion of app usage logs, patient-involved methods, and other approaches to determine the impact of mHealth is an important step forward in health intervention research. We hope that this overview will become a catalogue of the possible ways in which mHealth has been and can be integrated into research practice.

This trial aimed to evaluate the influence of two educational methods on students' ability to remove artificial carious dentine. Traditional lecture and lecture plus a live demonstration of artificial carious tissue removal were compared in a blind two-parallel-group design. Twenty-six students were randomly divided into two groups, and their skills were evaluated according to the following criteria: time spent on the dentine excavation procedure (in min), students' perceived confidence in conducting the procedure (graded assessed on a scale from 0 to 10), and the outcome of artificial carious tissue removal, evaluated by measuring the residual dyed artificial carious dentine layer (in μm). Statistical analyses were carried out using a t-test to compare the students' confidence and time spent on the procedure, and a two-way ANOVA was used to compare residual artificial decayed dentine with educational methods and tooth region (incisal, medium, and cervical thirds) as factors. There were no differences between the methods regarding excavation time (P = 0.898) and students' confidence (P = 0.382). The residual artificial carious dentine results showed that the educational method (P < 0.001) and cavity region (P < 0.001) were statistically significant, as was their interaction (P = 0.040). The lecture plus live demonstration group presented the best results for artificial caries removal. Although there were no differences between the two groups for the cervical region, the best results for the lecture plus live demonstration group was in the other two-thirds of the tooth.